Revealing Dynamic Communities in networks using genetic algorithm with Merging and Splitting Operators

TL;DR

This paper introduces a novel evolutionary algorithm with merging and splitting operators to accurately detect evolving community structures in dynamic networks, overcoming resolution limits and requiring no prior community number.

Contribution

The paper presents a new evolutionary method that combines a specialized fitness function with merging and splitting operators for dynamic community detection.

Findings

Outperforms state-of-the-art methods on model networks

Effective in real-world dynamic network scenarios

Does not require pre-specifying the number of communities

Abstract

Community structure is pervasive in various real-world networks, portraying the strong local clustering of nodes. Unveiling the community structure of a network is deemed to a crucial step towards understanding the dynamics on the network. Actually, most of the real-world networks are dynamic and their community structures are evolutionary over time accordingly. How to revealing the dynamical communities has recently become a pressing issue. Here, we present an evolutionary method for accurately identifying dynamical communities in the networks. In this method, we first introduced a fitness function that is a compound of asymptotic surprise values on the current and previous snapshots of the network. Second, we developed ad hoc merging and splitting operators, which allows for large-scale searching while preserving low cost. Third, this large-scale searching coupled with local mutation and crossover enhanced revealing a better solution to each snapshot of the network. This method does not require specifying the number of communities advanced, and free from resolution limit while satisfying temporal smooth constraint. Experimental results on both model and real dynamic networks show that the method can find a better solution compared with state-of-art approaches.